This article updates the guidance published in 2015 for authors submitting papers to British Journal of Pharmacology (Curtis et al., 2015) and is intended to provide the rubric for peer review. Thus, it is directed towards authors, reviewers and editors. Explanations for many of the requirements were outlined previously and are not restated here. The new guidelines are intended to replace those published previously. The guidelines have been simplified for ease of understanding by authors, to make it more straightforward for peer reviewers to check compliance and to facilitate the curation of the journal's efforts to improve standards.

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Reproducibility is a current concern for everyone involved in the conduct and publication of biomedical research. Recent attempts testing reproducibility, particularly the reproducibility project in cancer biology published in elife (https://elifesciences.org/collections/9b1e83d1/reproducibility-project-cancer-biology), have exposed major difficulties in repeating published preclinical experimental work. It is thought that some of these difficulties relate to uncertainty about the provenance of tools, lack of clarity in methodology and use of inappropriate approaches for analysis; the latter particularly related to untoward manipulation of images. In the past, some of these so-called untoward practices were considered the 'norm'; however, today, the landscape is different. The expectations, not only of the readers of the published scientific word but also of the publishers and funders of research, have changed. This collective group now expects that any published data should be reproducible; but for this to be possible, experimental detail, confirmation of selectivity and quality of reagents/ tools, analytical and statistical methods used need to be described adequately. Two powerful methodologies often used to support researchers' findings allow the detection of changes in protein expression, that is, immunoblotting (widely known as Western blotting) and immunohistochemistry. Undeniably, as a result of unintentional mistakes (often related to lack of antibody specificity; Baker, 2015), but, in some cases, deliberate alterations and questionable interpretations of results, the use of these two methods has led to many high profile retractions. Indeed, such images have driven the retractions that have occurred in BJP over the last two years.Today, immunoblotting and immunohistochemistry serve as primary methodologies for the detection and quantification of molecular signalling pathways and identification of therapeutic targets. This necessitates clear guidance for the application of these techniques, the need for controls (both positive and negative) and the most appropriate methods for quantification. Indeed, this need has spawned a number of initiatives to support researchers in assessing the validity of antibody resources including antibodypedia (Bjorling and Uhlen, 2008) and the resources available within 'The Human Protein Atlas' (Thul et al., 2017). The aim of this article is to outline the rationale for, and the expectations of, the BJP with respect to work published in the Journal that includes immunoblotting or immunohistochemical data. In creating these guidelines, our aim is to reduce potential misinterpretations and to maximise the communication and transparency of essential information, particularly with respect to the methodologies employed.We have generated the guidelines below for the benefit of authors, editors and reviewers. While we recognise other recently published guidelines (Uhlen et al., 2016) and indeed we have incorporated some of the advice provided in such reports, we focus, here, on th...

OBJECTIVES: The goal of this study was to develop an algorithm based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), codes for classifying children with chronic disease (CD) according to level of medical complexity and to assess the algorithm’s sensitivity and specificity. METHODS: A retrospective observational study was conducted among 700 children insured by Washington State Medicaid with ≥1 Seattle Children’s Hospital emergency department and/or inpatient encounter in 2010. The gold standard population included 350 children with complex chronic disease (C-CD), 100 with noncomplex chronic disease (NC-CD), and 250 without CD. An existing ICD-9-CM–based algorithm called the Chronic Disability Payment System was modified to develop a new algorithm called the Pediatric Medical Complexity Algorithm (PMCA). The sensitivity and specificity of PMCA were assessed. RESULTS: Using hospital discharge data, PMCA’s sensitivity for correctly classifying children was 84% for C-CD, 41% for NC-CD, and 96% for those without CD. Using Medicaid claims data, PMCA’s sensitivity was 89% for C-CD, 45% for NC-CD, and 80% for those without CD. Specificity was 90% to 92% in hospital discharge data and 85% to 91% in Medicaid claims data for all 3 groups. CONCLUSIONS: PMCA identified children with C-CD (who have accessed tertiary hospital care) with good sensitivity and good to excellent specificity when applied to hospital discharge or Medicaid claims data. PMCA may be useful for targeting resources such as care coordination to children with C-CD.

Scientists who plan to publish in British Journal of Pharmacology (BJP) must read this article before undertaking a study. This editorial provides guidance for the design of experiments. We have published previously two guidance documents on experimental design and analysis (Curtis et al., 2015; Curtis et al., 2018). This update clarifies and simplifies the requirements on design and analysis for BJP manuscripts. This editorial also details updated requirements following an audit and discussion on best practice by the BJP editorial board. Explanations for the requirements are provided in the previous articles. Here, we address new issues that have arisen in the course of handling manuscripts and emphasise three aspects of design that continue to present the greatest challenge to authors: randomisation, blinded analysis and balance of group sizes.